\section{Conclusions and further work}
We have investigated the potential of using picture books for
young children as datasets for automatic generation of word-image
lexicons. Our choice was motivated by the simple scenes and text
and the simple logical relations between them, that are typically found
in such books. 

Our experiments indicate that such small datasets are not optimal
for unsupervised learning techniques. Parts of our system performs very
well, the NLP system is capable of picking fairly relevant keywords, the
object-detection system works well and the image-recognition system
works well together with the part of the reasoning system that group
blobs together. It is possible that an implementation of the greedy
label assigning algorithm described by Makadia el al \cite{makadia2008}
would give our system results good enough to at least be measured in
the standard terms of \emph{precision} and \emph{recall}. But, we are
convinced that such an effort would be a waste of time. If one wants to
create a simple and well performing system, one should use a large dataset for
training it. And since such large datasets today easily can be composed
from an abundance of web data, the only reason not to do so is a
possible lack of computing power.

The field of automatic image annotation is interesting and will probably
contribute to exiting products in the future. We feel that the field has
now reached a level of maturity where it would be interesting to work
with real world data from web news articles or from the wikipedia.
Specifically, it would be interesting to test a combination of the
keyword extraction system by Deschact and Moens\cite{deschacht-moens}
and Tagprop\cite{tagprop}.

\begin{comment}
\subsection{What have we (and you) learned from your work?}
\subsection{How might it help related efforts?}
\subsection{If you had more time, what further work would you do?}
Synpunkter fran Demo:
* What would be required to get a working system with good performance? Anknyt
till state of the art forskning?
* Why do we need a large data set? Childre don't. - Unsupervised learning.
\end{comment}

